No abstract

Due to the long cold start time and slow dynamics of proton exchange membrane (PEM) fuel cell (FC) stack, operating modes transfer control strategy for fuel cell uninterruptible power supply (FC-UPS) is different from the traditional uninterruptible power supply (UPS) system. In this paper, a seamless transfer control strategy, which is suitable for FC-UPS, is proposed. The power conversion architecture of FC-UPS is presented with the characteristic analysis of PEMFC and the requirements of UPS. Then, the scheme of the seamless transfer control strategy is investigated. The proposed seamless transfer control strategy is not only capable of guaranteeing the uninterruptible load voltage, but also protecting FC against the power demands beyond its allowable bandwidth during the transition for long lifespan and safety. Finally, the control scheme has been verified on a 10-kW FC-UPS prototype.Index Terms-Cold start, fuel cell (FC), power management unit (PMU), seamless transfer, uninterruptible power supply (UPS).

Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.

Sign language recognition is a growing research area in the field of computer vision. A challenge within it is to model various signs, varying with time resolution, visual manual appearance, and so on. In this paper, we propose a discriminative exemplar coding (DEC) approach, as well as utilizing Kinect sensor, to model various signs. The proposed DEC method can be summarized as three steps. First, a quantity of class-specific candidate exemplars are learned from sign language videos in each sign category by considering their discrimination. Then, every video of all signs is described as a set of similarities between frames within it and the candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by a set of exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video is treated as a positive (or negative) bag and those frames similar to the given exemplar in Euclidean space as instances. Finally, we formulate the selection of the most discriminative exemplars into a framework and simultaneously produce a sign video classifier to recognize sign. To evaluate our method, we collect an American sign language dataset, which includes approximately 2000 phrases, while each phrase is captured by Kinect sensor with color, depth, and skeleton information. Experimental results on our dataset demonstrate the feasibility and effectiveness of the proposed approach for sign language recognition.